Why learn to code as a cancer researcher?

• Reproducible data analysis:

  • Document and share exactly how you analyzed your data

  • Do more with your analysis, more efficiently:

  • More control and flexibility

  • Use community-created analysis tools

  • Leverage cancer data resources (often large datasets)

  • Create awesome visualizations

Imagined learning curve

Our view of the learning curve

Course goals

• Get everyone past ‘valley of despair’ in R learning curve

• Convince you that R is an accessible and useful tool for you in your research

• Prepare you to tackle BootCamp projects next week

• Get you excited to keep developing these coding skills!

Why R specifically

Upsides

• Free

• Great for data analysis and visualization

• LOTS of bioinformatics/stats tools available

Downsides?

• It’s a hodge-podge

• Not the best for engineering software

But I have excel

Quick demo

[LIVE DEMO]

Strategy of the course

• What this course is

  • Coding basics

  • Heavy emphasis on practical skills (data wrangling, visualization)

  • Flagging areas with technical depth but giving the ‘need-to-know’

• What this course is not

  • Intro to computer science

  • Intro to stats

Other helpful R resources

• Hadley Wickham’s R for Data Science e-book

• R cheat sheets

• Rafael Irizarry’s R e-book

• and much more…

Acknowledgements

• Chester Ismay’s DataCamp slides

• HBC Intro to R course

• Sam Meier’s 2018 R lectures

• Hadley Wickham’s R for Data Science

We need your help

• Your feedback is very much appreciated (Slack, email, etc)

• We’ll do our best to adapt as we go.

Key Tools

• R Markdown/Notebooks

  • Sort of like a lab notebook for analysis

  • Easily share results and methods in different formats

  • Encourages good code and analysis practices

The R console

Where the action happens!

• Provide inputs to R

• See outputs of commands you give it (each on separate line)

Create project directory

• Organize your work as ‘Projects’ in Rstudio

• Each project has a separate folder, with data, code, results.

• Create a ‘Project’ in Rstudio

R as calculator

List of key math operations

• *: multiplication
• /: division
• +: addition
• -: subtraction
• ^: ‘raise to the power’

Logical operations

• == Check equals
• != Check not equals
• > Greater than, < less than
• >= Greater than or equal to, etc.

2^2 == 3

[1] FALSE

Objects in R

• Variables:

  • Store information/data (the “nouns”)
  • Come in a number of flavors

• Functions:

  • Set of instructions to perform some task (the “verbs”)
  • We’ll come back to these in a bit

Creating variables

• You can create new variables with <-

x <- 3 * 4
x

[1] 12

• All object-creation statements have the form

  • object_name <- value

  • You can use =, but <- makes for better R code

color1 = 'red'
color2 <- 'red'
color1 == color2

[1] TRUE

Variable naming

• Variable names must start with a letter, and can only contain letters, numbers, ‘_’, and ‘.’.

Not allowed: 4th, my var, weird?, etc. etc.

• Object naming is important for writing good, readable, code

• Make variable names descriptive

avgClicks

• Make function names verbs

calculate_avg_clicks

NOT: var1 or a

Use comments!

• Code readability is huge so others can understand what you’ve done

  • Including future you!

• In RMarkdown docs, write descriptive text before each code chunk

• Also good to add comments to key lines of code within chunks

R environment

• See info on current variables

• Clearing variables

• View data tables, etc.

Commonly used R data structures

• Vectors

• Lists

• Matrix

• Dataframes

Making vectors

• Use ‘combine’ function c()

num_vec <- c(1, 2, 3, 4)

log_vec <- c(TRUE, TRUE, FALSE, F)

str_vec <- c('this', 'is', 'a', 'vector', 'of', 'strings')

print(num_vec)

[1] 1 2 3 4

• Shorthand to create a sequence of integers

1:4

[1] 1 2 3 4

Missing values

• Quick notes on missing values in R (will be important)

• NA (‘not available’) is a special value for missing data that can be included in any type of vector

c(1, 2, NA)

[1]  1  2 NA

c('a', 'b', NA)

[1] "a" "b" NA 

Lists

Lists are basically like relaxed vectors, where elements don’t have to be the same type

z <- list('a', 1, TRUE)
z

[[1]]
[1] "a"

[[2]]
[1] 1

[[3]]
[1] TRUE

You can combine lists with the c() function as with vectors

z <- list('a', 1, TRUE)
c(z, 'c')

[[1]]
[1] "a"

[[2]]
[1] 1

[[3]]
[1] TRUE

[[4]]
[1] "c"

Matrix

• Like vectors, but arranged in 2d with rows and columns

• Example: gene expression values across genes and samples

Dataframe

• Most common way of interacting with data

• Each column is a vector, and they can hold different kinds of data

• Like an Excel table.

Key concepts recap

• Use RMarkdown documents like an experiment notebook

• Create variables with <-, naming them is important

• Variables can be numbers, text (strings) or TRUE/FALSE (boolean)

• Data organized as vectors, lists, matrices, and dataframes

• Create/add to vectors (or lists) with c()

• Make lists with list()